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Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo

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Abstract

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

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Acknowledgements

D.L.M. appreciates financial support from the National Institutes of Health (R01GM108889 and R01GM132386). DLM and YG also appreciate financial support from XtalPi. We appreciate the Open Force Field Consortium for its support of the Open Force Field Initiative, which provided software infrastructure used in this work.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA

    Yunhui Ge, Weiming Dong & David L. Mobley

  2. School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK

    Oliver J. Melling & Jonathan W. Essex

  3. Department of Chemistry, University of California,Irvine, Irvine, CA, 92697, USA

    David L. Mobley

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  1. Yunhui Ge
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  2. Oliver J. Melling
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DLM is a member of the Scientific Advisory Boards of OpenEye Scientific Software and Anagenex and is an Open Science Fellow with Roivant.

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Correspondence to David L. Mobley.

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Ge, Y., Melling, O.J., Dong, W. et al. Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo. J Comput Aided Mol Des 36, 767–779 (2022). https://doi.org/10.1007/s10822-022-00479-w

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